In data centers where there are information technology (IT) devices, such as computers, servers, and routers, accommodated in rack devices, heated air is taken out and cooling air is taken in using air conditioners so that the IT devices are cooled down.
In recent years, the temperature in the data centers tend to become high because the IT devices are closely arranged in rack devices and the consumed power of the data centers have increased. As a result, sometimes the IT devices are not cooled down sufficiently, which causes problems. When the temperature distribution in data centers is examined, it is known that the IT devices and the rack devices are arranged at different locations in different installation environments, and that the amount of heat generated by the central processing units (CPUs) in the IT devices varies, and the amount of heat generated by the rack devices varies. Therefore, some IT devices are not cooled down, and heated beyond a permissible temperature. Furthermore, such an increase in the temperature in a data center may cause a malfunction of the air conditioners that supply the cooling air; for example, abnormal operation or abnormal stop of the air conditioner may occur.
To predict the occurrence of such an abnormal condition, a sensor or the like is employed (see, for example, Japanese Laid-open Patent Publication No. 2008-034715, and Japanese Laid-open Patent Publication No. 08-095672). This prediction technique using a sensor or the like, however, takes a long time to rectify the problems. This is because this prediction technique using a sensor predicts an abnormal phenomenon, which would cause a problem, based on measurements at limited measurement points, and identifies and analyzes factors causing the abnormal condition and then formulate a countermeasure for the problem making full use of various types of thermal technologies and analysis technologies.
In view of this drawback, a technique utilizing thermal-fluid simulation is employed for predicting an abnormal phenomenon other than the above-described prediction technique utilizing the sensor. Specifically, when a temperature sensor, an air flow sensor, or the like detects abnormal temperature, abnormal air flow, or the like in the data center, various actions are taken: for example, the measurement data such as temperature data is analyzed for identifying the problem; the situation is assessed based on a simulation; and a countermeasure is taken.
For example, a simulator, which conducts a thermal fluid simulation, performs a series of processes to assess the situation based on a thermal-fluid simulation: for example, the simulator performs a series of processes for collecting an analysis conditions, formulating an analysis model, generating a mesh, executing calculations, and analyzing a result.
Each of these processes is described in detail below. The simulator first collects analysis conditions that include floor information, information about the arrangement of rack devices, grilles, and air conditioners, amounts of rack-generated heat, volumes of rack exhaust-air, grille aperture ratios, cooling performances of the air conditioners, and flow volumes of the air conditioners. The simulator obtains the analysis conditions from, for example, a database which is created by an administrator and which contains information about arrangement of devices inside the data center. The simulator then performs model shaping, heat-generating-condition setting, and air-flow setting, to generate an analysis model.
After that, the simulator divides the analysis model into meshes by transforming conservation equations (e.g., the Navier-Stokes equation) of the air flow and the thermal transfer in the data center into finite volumes. If the space to be analyzed is large or if a highly accurate simulation is needed, the number of meshes is increased and the number of calculations is increased.
After that, the simulator performs coupling so that one parameter of the conservation equation, e.g., the Navier-Stokes equation, depends on another parameter or a nearby value and then performs a finite iterative calculation repeatedly until the error of the conservation equation decreases to an allowable level, thereby causing the solutions to converge and thus solving the conservation equation. More specifically, the simulator causes the solutions of the Navier-Stokes equation to converge using a widely-known splitting method, for example, Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorism.
For example, after setting the default values in the conservation equation, the simulator calculates, using the conservation equation, both the flow velocity in the x axis of the mesh-divided space and the flow velocity in the y axis. The simulator then calculates the pressure in the mesh-divided space, updates the flow velocity and the density for the conservation equation, and calculates the temperature, for example, using another equation, e.g., the heat equation. The simulator determines whether an error between a threshold and a boundary value that is determined according to the calculated flow velocities and the calculated temperature is within an allowable range, i.e., whether a converged solution is obtained for the boundary value. If a converged solution is obtained for the boundary value, the simulator ends the simulation. If a converged solution is not obtained for the boundary value, the simulator repeats the above simulation until a converged solution is obtained for the boundary value. After the simulation ends, the administrator, etc., assesses the current situation using the simulation result.
However, in the conventional technique described above, it takes a significantly long time for the simulation that is conducted to analyze the air-conditioning state in the data center; therefore, analysis of the situation is not made quickly. When a problem occurs in the data center, the situation can be analyzed and assessed only after the series of processes described above is performed. Then, it takes a significantly long time to analyze the situation and to take countermeasures for the problem.
For example, assume that it is necessary for the conventional simulator to repeat the finite iterative calculation about 200 times until the error of the conservation equation decreases to an allowable level, as illustrated in FIG. 15. In this case, when a temperature problem occurs in the data center, the conventional simulator initializes previously calculated results after collecting the analysis conditions and generating the analysis model as describe earlier. Thereafter, the conventional simulator performs a calculation about 200 times to create the information for analyzing the situation. Therefore, it takes a long time for the administrator, etc., to analyze the situation and, even longer time to take countermeasures for the problem.